This research demonstrates how cloud processes, steep mountains, tropical coastlines, the daily changes in solar insolation and planetary-scale waves work together to cause large variations in the tropical heating that drives global circulation patterns. Many of these effects are under-represented in global climate models.

The MAAT framework can be used to systematically run multiple model simulations to explore how different underlying model assumptions, hypotheses and parameters lead to predicted model behaviour and isolate the causes of model divergence.

It is hoped this proposed synthesis of two ENSO structures, their interaction with each other and how they respond to external forcing, will be the catalyst for future research and practical applications for forecasting and determining the impacts of present and future ENSO events.

This study highlights the importance of simulating global and regional warming responses correctly, to enable more accurate estimates of how the occurrence probability of climate extremes may change in a warming climate.

New research clearly demonstrates the potential to predict long-term LAI using simple ecohydrological theory. This approach could potentially be incorporated into existing terrestrial biosphere models and help improve predictions of LAI.

Over four years, the Norwegian Polar Institute’s (NPI) Ocean and Sea Ice team used the social media handle @oceanseaicenpi across Instagram, Twitter and Facebook to communicate its research to peers and the public.

In relation to the Paris Agreement targets of 1.5°C and 2°C, new research shows the differences in results between pattern-scaling and climate model output were primarily due to forcings other than changes to greenhouse gas emissions.

This research suggests some trees and in particular, Australian trees, may be more resilient than expected to future warming and extreme events. These findings have implications for planning around which species to plant in “green cities” to help mitigate future climate extremes.

The application of a simple carbon balance model, combined with a data assimilation approach, has the potential to improve the process understanding embedded in models, which is used to predict responses of the carbon cycle to climate change.

Convective parameterizations are widely believed to be essential for realistic simulations of the atmosphere, but are crude in today’s weather and climate models. CLEX researchers, report on what happens when a number of these models are run with these schemes simply turned off.